{"id":2637,"date":"2024-10-16T10:34:12","date_gmt":"2024-10-16T10:34:12","guid":{"rendered":"https:\/\/blog.examboosts.com\/?p=2637"},"modified":"2024-10-16T10:34:12","modified_gmt":"2024-10-16T10:34:12","slug":"oct-2024-free-professional-data-engineer-exam-dumps-to-improve-exam-score-q144-q159","status":"publish","type":"post","link":"https:\/\/blog.examboosts.com\/zh\/2024\/10\/oct-2024-free-professional-data-engineer-exam-dumps-to-improve-exam-score-q144-q159\/","title":{"rendered":"[Oct-2024] Free Professional-Data-Engineer Exam Dumps to Improve Exam Score [Q144-Q159]"},"content":{"rendered":"\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-left kksr-valign-top\"\n    data-payload='{&quot;align&quot;:&quot;left&quot;,&quot;id&quot;:&quot;2637&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;top&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;Rate this post&quot;,&quot;legend&quot;:&quot;0\\\/5 - (0 votes)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;[Oct-2024] Free Professional-Data-Engineer Exam Dumps to Improve Exam Score [Q144-Q159]&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} - ({count} {votes})&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 19.2px;\">\n            <span class=\"kksr-muted\">Rate this post<\/span>\n    <\/div>\n    <\/div>\n<p><span style=\"color: red\"><strong><span style=\"font-size: 18px\">[Oct-2024] Free Professional-Data-Engineer Exam Dumps to Improve Exam Score<\/span><\/strong><\/span><\/p>\n<p><span style=\"color: red\"><strong>2024 Realistic Professional-Data-Engineer Dumps Exam Tips Test Pdf Exam Material<\/strong><\/span><\/p>\n<div id=\"watu_quiz\" class=\"quiz-area single-page-quiz\">\n<form action=\"\" method=\"post\" class=\"quiz-form \" id=\"quiz-1011\" >\n<div class='watu-question' id='question-1'><div class='question-content'><p><strong>NEW QUESTION 144<\/strong><br \/>You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster&#8217;s local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc?<br \/>(Choose two.)<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19970' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77558' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19970[]' id='answer-id-77558' class='answer answer-1 js-answer-label answerof-19970' value='77558' \/>&nbsp;<label for='answer-id-77558' id='answer-label-77558' class='js-answer-label answer label-1'><span class='answer'>Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77559' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19970[]' id='answer-id-77559' class='answer answer-1 php-answer-label answerof-19970' value='77559' \/>&nbsp;<label for='answer-id-77559' id='answer-label-77559' class='php-answer-label answer label-1'><span class='answer'>Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77560' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19970[]' id='answer-id-77560' class='answer answer-1 php-answer-label answerof-19970' value='77560' \/>&nbsp;<label for='answer-id-77560' id='answer-label-77560' class='php-answer-label answer label-1'><span class='answer'>Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77561' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19970[]' id='answer-id-77561' class='answer answer-1 js-answer-label answerof-19970' value='77561' \/>&nbsp;<label for='answer-id-77561' id='answer-label-77561' class='js-answer-label answer label-1'><span class='answer'>Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables.<br \/>Replicate external Hive tables to the native ones.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77562' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19970[]' id='answer-id-77562' class='answer answer-1 js-answer-label answerof-19970' value='77562' \/>&nbsp;<label for='answer-id-77562' id='answer-label-77562' class='js-answer-label answer label-1'><span class='answer'>Load the ORC files into BigQuery. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>HDFS lies on datanode, data on masternode needs to be copied on datanode.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(1,this)' id='btn-1' value='See Answer'  \/><input type='hidden' id='questionType1' value='checkbox' class=''><\/div><div class='watu-question' id='question-2'><div class='question-content'><p><strong>NEW QUESTION 145<\/strong><br \/>You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19971' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77563' \/><div class='watu-question-choice'><input type='radio' name='answer-19971[]' id='answer-id-77563' class='answer answer-2 js-answer-label answerof-19971' value='77563' \/>&nbsp;<label for='answer-id-77563' id='answer-label-77563' class='js-answer-label answer label-2'><span class='answer'>Specify a worker region by using the -region flag.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77564' \/><div class='watu-question-choice'><input type='radio' name='answer-19971[]' id='answer-id-77564' class='answer answer-2 php-answer-label answerof-19971' value='77564' \/>&nbsp;<label for='answer-id-77564' id='answer-label-77564' class='php-answer-label answer label-2'><span class='answer'>Set the pipeline staging location as a regional Cloud Storage bucket.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77565' \/><div class='watu-question-choice'><input type='radio' name='answer-19971[]' id='answer-id-77565' class='answer answer-2 js-answer-label answerof-19971' value='77565' \/>&nbsp;<label for='answer-id-77565' id='answer-label-77565' class='js-answer-label answer label-2'><span class='answer'>Submit duplicate pipelines in two different zones by using the -zone flag.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77566' \/><div class='watu-question-choice'><input type='radio' name='answer-19971[]' id='answer-id-77566' class='answer answer-2 js-answer-label answerof-19971' value='77566' \/>&nbsp;<label for='answer-id-77566' id='answer-label-77566' class='js-answer-label answer label-2'><span class='answer'>Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>By specifying a worker region, you can run your Dataflow pipeline in a multi-zone or multi-region configuration, which provides higher availability and resilience in case of zonal failures1. The -region flag allows you to specify the regional endpoint for your pipeline, which determines the location of the Dataflow service and the default location of the Compute Engine resources1. If you do not specify a zone by using the -zone flag, Dataflow automatically selects a zone within the region for your job workers1. This option is recommended over submitting duplicate pipelines in two different zones, which would incur additional costs and complexity. Setting the pipeline staging location as a regional Cloud Storage bucket does not affect the availability of your pipeline, as the staging location only stores the pipeline code and dependencies2. Creating an Eventarc trigger to resubmit the job in case of zonal failure is not a reliable solution, as it depends on the availability of the Eventarc service and the zonal resources at the time of resubmission. Reference:<br\/>1: Pipeline troubleshooting and debugging | Cloud Dataflow | Google Cloud<br\/>3: Regional endpoints | Cloud Dataflow | Google Cloud<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(2,this)' id='btn-2' value='See Answer'  \/><input type='hidden' id='questionType2' value='radio' class=''><\/div><div class='watu-question' id='question-3'><div class='question-content'><p><strong>NEW QUESTION 146<\/strong><br \/>MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last<br \/>2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19972' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77567' \/><div class='watu-question-choice'><input type='radio' name='answer-19972[]' id='answer-id-77567' class='answer answer-3 js-answer-label answerof-19972' value='77567' \/>&nbsp;<label for='answer-id-77567' id='answer-label-77567' class='js-answer-label answer label-3'><span class='answer'>Rowkey: date#device_idColumn data: data_point<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77568' \/><div class='watu-question-choice'><input type='radio' name='answer-19972[]' id='answer-id-77568' class='answer answer-3 js-answer-label answerof-19972' value='77568' \/>&nbsp;<label for='answer-id-77568' id='answer-label-77568' class='js-answer-label answer label-3'><span class='answer'>Rowkey: dateColumn data: device_id, data_point<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77569' \/><div class='watu-question-choice'><input type='radio' name='answer-19972[]' id='answer-id-77569' class='answer answer-3 js-answer-label answerof-19972' value='77569' \/>&nbsp;<label for='answer-id-77569' id='answer-label-77569' class='js-answer-label answer label-3'><span class='answer'>Rowkey: device_idColumn data: date, data_point<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77570' \/><div class='watu-question-choice'><input type='radio' name='answer-19972[]' id='answer-id-77570' class='answer answer-3 php-answer-label answerof-19972' value='77570' \/>&nbsp;<label for='answer-id-77570' id='answer-label-77570' class='php-answer-label answer label-3'><span class='answer'>Rowkey: data_pointColumn data: device_id, date<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77571' \/><div class='watu-question-choice'><input type='radio' name='answer-19972[]' id='answer-id-77571' class='answer answer-3 js-answer-label answerof-19972' value='77571' \/>&nbsp;<label for='answer-id-77571' id='answer-label-77571' class='js-answer-label answer label-3'><span class='answer'>Rowkey: date#data_pointColumn data: device_id<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(3,this)' id='btn-3' value='See Answer'  \/><input type='hidden' id='questionType3' value='radio' class=''><\/div><div class='watu-question' id='question-4'><div class='question-content'><p><strong>NEW QUESTION 147<\/strong><br \/>You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19973' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77572' \/><div class='watu-question-choice'><input type='radio' name='answer-19973[]' id='answer-id-77572' class='answer answer-4 js-answer-label answerof-19973' value='77572' \/>&nbsp;<label for='answer-id-77572' id='answer-label-77572' class='js-answer-label answer label-4'><span class='answer'>Assign the users\/groups data viewer access at the table level for each table<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77573' \/><div class='watu-question-choice'><input type='radio' name='answer-19973[]' id='answer-id-77573' class='answer answer-4 js-answer-label answerof-19973' value='77573' \/>&nbsp;<label for='answer-id-77573' id='answer-label-77573' class='js-answer-label answer label-4'><span class='answer'>Create SQL views for each team in the same dataset in which the data resides, and assign the users\/groups data viewer access to the SQL views<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77574' \/><div class='watu-question-choice'><input type='radio' name='answer-19973[]' id='answer-id-77574' class='answer answer-4 php-answer-label answerof-19973' value='77574' \/>&nbsp;<label for='answer-id-77574' id='answer-label-77574' class='php-answer-label answer label-4'><span class='answer'>Create authorized views for each team in the same dataset in which the data resides, and assign the users\/groups data viewer access to the authorized views<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77575' \/><div class='watu-question-choice'><input type='radio' name='answer-19973[]' id='answer-id-77575' class='answer answer-4 js-answer-label answerof-19973' value='77575' \/>&nbsp;<label for='answer-id-77575' id='answer-label-77575' class='js-answer-label answer label-4'><span class='answer'>Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users\/groups data viewer access to the datasets in which the authorized views reside<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(4,this)' id='btn-4' value='See Answer'  \/><input type='hidden' id='questionType4' value='radio' class=''><\/div><div class='watu-question' id='question-5'><div class='question-content'><p><strong>NEW QUESTION 148<\/strong><br \/>You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19974' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77576' \/><div class='watu-question-choice'><input type='radio' name='answer-19974[]' id='answer-id-77576' class='answer answer-5 js-answer-label answerof-19974' value='77576' \/>&nbsp;<label for='answer-id-77576' id='answer-label-77576' class='js-answer-label answer label-5'><span class='answer'>Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77577' \/><div class='watu-question-choice'><input type='radio' name='answer-19974[]' id='answer-id-77577' class='answer answer-5 php-answer-label answerof-19974' value='77577' \/>&nbsp;<label for='answer-id-77577' id='answer-label-77577' class='php-answer-label answer label-5'><span class='answer'>Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77578' \/><div class='watu-question-choice'><input type='radio' name='answer-19974[]' id='answer-id-77578' class='answer answer-5 js-answer-label answerof-19974' value='77578' \/>&nbsp;<label for='answer-id-77578' id='answer-label-77578' class='js-answer-label answer label-5'><span class='answer'>Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77579' \/><div class='watu-question-choice'><input type='radio' name='answer-19974[]' id='answer-id-77579' class='answer answer-5 js-answer-label answerof-19974' value='77579' \/>&nbsp;<label for='answer-id-77579' id='answer-label-77579' class='js-answer-label answer label-5'><span class='answer'>Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(5,this)' id='btn-5' value='See Answer'  \/><input type='hidden' id='questionType5' value='radio' class=''><\/div><div class='watu-question' id='question-6'><div class='question-content'><p><strong>NEW QUESTION 149<\/strong><br \/>Flowlogistic Case Study<br \/>Company Overview<br \/>Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.<br \/>Company Background<br \/>The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.<br \/>Solution Concept<br \/>Flowlogistic wants to implement two concepts using the cloud:<br \/>* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads<br \/>* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.<br \/>Existing Technical Environment<br \/>Flowlogistic architecture resides in a single data center:<br \/>* Databases<br \/>* 8 physical servers in 2 clusters<br \/>* SQL Server &#8211; user data, inventory, static data<br \/>* 3 physical servers<br \/>* Cassandra &#8211; metadata, tracking messages<br \/>10 Kafka servers &#8211; tracking message aggregation and batch insert<br \/>* Application servers &#8211; customer front end, middleware for order\/customs<br \/>* 60 virtual machines across 20 physical servers<br \/>* Tomcat &#8211; Java services<br \/>* Nginx &#8211; static content<br \/>* Batch servers<br \/>Storage appliances<br \/>* iSCSI for virtual machine (VM) hosts<br \/>* Fibre Channel storage area network (FC SAN) &#8211; SQL server storage<br \/>* Network-attached storage (NAS) image storage, logs, backups<br \/>* 10 Apache Hadoop \/Spark servers<br \/>* Core Data Lake<br \/>* Data analysis workloads<br \/>* 20 miscellaneous servers<br \/>* Jenkins, monitoring, bastion hosts,<br \/>Business Requirements<br \/>* Build a reliable and reproducible environment with scaled panty of production.<br \/>* Aggregate data in a centralized Data Lake for analysis<br \/>* Use historical data to perform predictive analytics on future shipments<br \/>* Accurately track every shipment worldwide using proprietary technology<br \/>* Improve business agility and speed of innovation through rapid provisioning of new resources<br \/>* Analyze and optimize architecture for performance in the cloud<br \/>* Migrate fully to the cloud if all other requirements are met<br \/>Technical Requirements<br \/>* Handle both streaming and batch data<br \/>* Migrate existing Hadoop workloads<br \/>* Ensure architecture is scalable and elastic to meet the changing demands of the company.<br \/>* Use managed services whenever possible<br \/>* Encrypt data flight and at rest<br \/>* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.<br \/>We need to organize our information so we can more easily understand where our customers are and what they are shipping.<br \/>CTO Statement<br \/>IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO&#8217; s tracking technology.<br \/>CFO Statement<br \/>Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don&#8217;t want to commit capital to building out a server environment.<br \/>Flowlogistic&#8217;s management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19975' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77580' \/><div class='watu-question-choice'><input type='radio' name='answer-19975[]' id='answer-id-77580' class='answer answer-6 js-answer-label answerof-19975' value='77580' \/>&nbsp;<label for='answer-id-77580' id='answer-label-77580' class='js-answer-label answer label-6'><span class='answer'>Cloud Pub\/Sub, Cloud Dataflow, and Cloud Storage<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77581' \/><div class='watu-question-choice'><input type='radio' name='answer-19975[]' id='answer-id-77581' class='answer answer-6 js-answer-label answerof-19975' value='77581' \/>&nbsp;<label for='answer-id-77581' id='answer-label-77581' class='js-answer-label answer label-6'><span class='answer'>Cloud Pub\/Sub, Cloud Dataflow, and Local SSD<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77582' \/><div class='watu-question-choice'><input type='radio' name='answer-19975[]' id='answer-id-77582' class='answer answer-6 php-answer-label answerof-19975' value='77582' \/>&nbsp;<label for='answer-id-77582' id='answer-label-77582' class='php-answer-label answer label-6'><span class='answer'>Cloud Pub\/Sub, Cloud SQL, and Cloud Storage<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77583' \/><div class='watu-question-choice'><input type='radio' name='answer-19975[]' id='answer-id-77583' class='answer answer-6 js-answer-label answerof-19975' value='77583' \/>&nbsp;<label for='answer-id-77583' id='answer-label-77583' class='js-answer-label answer label-6'><span class='answer'>Cloud Load Balancing, Cloud Dataflow, and Cloud Storage<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(6,this)' id='btn-6' value='See Answer'  \/><input type='hidden' id='questionType6' value='radio' class=''><\/div><div class='watu-question' id='question-7'><div class='question-content'><p><strong>NEW QUESTION 150<\/strong><br \/>You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage. The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19976' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77584' \/><div class='watu-question-choice'><input type='radio' name='answer-19976[]' id='answer-id-77584' class='answer answer-7 js-answer-label answerof-19976' value='77584' \/>&nbsp;<label for='answer-id-77584' id='answer-label-77584' class='js-answer-label answer label-7'><span class='answer'>1. Deploy a long-living Dalaproc cluster with Apache Hive and Ranger enabled.<br \/>2. Configure Ranger for column level security.<br \/>3. Process with Dataproc Spark or Hive SQL.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77585' \/><div class='watu-question-choice'><input type='radio' name='answer-19976[]' id='answer-id-77585' class='answer answer-7 js-answer-label answerof-19976' value='77585' \/>&nbsp;<label for='answer-id-77585' id='answer-label-77585' class='js-answer-label answer label-7'><span class='answer'>1. Define a BigLake table.<br \/>2. Create a taxonomy of policy tags in Data Catalog.<br \/>3. Add policy lags to columns.<br \/>4. Process with the Spark-BigQuery connector or BigQuery SOL.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77586' \/><div class='watu-question-choice'><input type='radio' name='answer-19976[]' id='answer-id-77586' class='answer answer-7 js-answer-label answerof-19976' value='77586' \/>&nbsp;<label for='answer-id-77586' id='answer-label-77586' class='js-answer-label answer label-7'><span class='answer'>1. Load the data to BigQuery tables.<br \/>2. Create a taxonomy of policy tags in Data Catalog.<br \/>3. Add policy tags to columns.<br \/>4. Procoss with the Spark-BigQuery connector or BigQuery SQL.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77587' \/><div class='watu-question-choice'><input type='radio' name='answer-19976[]' id='answer-id-77587' class='answer answer-7 php-answer-label answerof-19976' value='77587' \/>&nbsp;<label for='answer-id-77587' id='answer-label-77587' class='php-answer-label answer label-7'><span class='answer'>1 Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage<br \/>2. Define a BigQuery external table for SQL processing.<br \/>3. Use Dataproc Spark to process the Cloud Storage files.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>For automating the CI\/CD pipeline of DAGs running in Cloud Composer, the following approach ensures that DAGs are tested and deployed in a streamlined and efficient manner.<br\/>Use Cloud Build for Development Instance Testing:<br\/>Use Cloud Build to automate the process of copying the DAG code to the Cloud Storage bucket of the development instance.<br\/>This triggers Cloud Composer to automatically pick up and test the new DAGs in the development environment.<br\/>Testing and Validation:<br\/>Ensure that the DAGs run successfully in the development environment.<br\/>Validate the functionality and correctness of the DAGs before promoting them to production.<br\/>Deploy to Production:<br\/>If the DAGs pass all tests in the development environment, use Cloud Build to copy the tested DAG code to the Cloud Storage bucket of the production instance.<br\/>This ensures that only validated and tested DAGs are deployed to production, maintaining the stability and reliability of the production environment.<br\/>Simplicity and Reliability:<br\/>This approach leverages Cloud Build&#8217;s capabilities for automation and integrates seamlessly with Cloud Composer&#8217;s reliance on Cloud Storage for DAG storage.<br\/>By using Cloud Storage for both development and production deployments, the process remains simple and robust.<br\/>Google Data Engineer Reference:<br\/>Cloud Composer Documentation<br\/>Using Cloud Build<br\/>Deploying DAGs to Cloud Composer<br\/>Automating DAG Deployment with Cloud Build<br\/>By implementing this CI\/CD pipeline, you ensure that DAGs are thoroughly tested in the development environment before being automatically deployed to the production environment, maintaining high quality and reliability.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(7,this)' id='btn-7' value='See Answer'  \/><input type='hidden' id='questionType7' value='radio' class=''><\/div><div class='watu-question' id='question-8'><div class='question-content'><p><strong>NEW QUESTION 151<\/strong><br \/>What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19977' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77588' \/><div class='watu-question-choice'><input type='radio' name='answer-19977[]' id='answer-id-77588' class='answer answer-8 js-answer-label answerof-19977' value='77588' \/>&nbsp;<label for='answer-id-77588' id='answer-label-77588' class='js-answer-label answer label-8'><span class='answer'>create a third instance and sync the data from the two storage types via batch jobs<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77589' \/><div class='watu-question-choice'><input type='radio' name='answer-19977[]' id='answer-id-77589' class='answer answer-8 php-answer-label answerof-19977' value='77589' \/>&nbsp;<label for='answer-id-77589' id='answer-label-77589' class='php-answer-label answer label-8'><span class='answer'>export the data from the existing instance and import the data into a new instance<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77590' \/><div class='watu-question-choice'><input type='radio' name='answer-19977[]' id='answer-id-77590' class='answer answer-8 js-answer-label answerof-19977' value='77590' \/>&nbsp;<label for='answer-id-77590' id='answer-label-77590' class='js-answer-label answer label-8'><span class='answer'>run parallel instances where one is HDD and the other is SDD<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77591' \/><div class='watu-question-choice'><input type='radio' name='answer-19977[]' id='answer-id-77591' class='answer answer-8 js-answer-label answerof-19977' value='77591' \/>&nbsp;<label for='answer-id-77591' id='answer-label-77591' class='js-answer-label answer label-8'><span class='answer'>the selection is final and you must resume using the same storage type<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Explanation<br\/>When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.<br\/>If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.<br\/>Reference: https:\/\/cloud.google.com\/bigtable\/docs\/choosing-ssd-hdd-<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(8,this)' id='btn-8' value='See Answer'  \/><input type='hidden' id='questionType8' value='radio' class=''><\/div><div class='watu-question' id='question-9'><div class='question-content'><p><strong>NEW QUESTION 152<\/strong><br \/>You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19978' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77592' \/><div class='watu-question-choice'><input type='radio' name='answer-19978[]' id='answer-id-77592' class='answer answer-9 js-answer-label answerof-19978' value='77592' \/>&nbsp;<label for='answer-id-77592' id='answer-label-77592' class='js-answer-label answer label-9'><span class='answer'>The current epoch time<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77593' \/><div class='watu-question-choice'><input type='radio' name='answer-19978[]' id='answer-id-77593' class='answer answer-9 js-answer-label answerof-19978' value='77593' \/>&nbsp;<label for='answer-id-77593' id='answer-label-77593' class='js-answer-label answer label-9'><span class='answer'>A concatenation of the product name and the current epoch time<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77594' \/><div class='watu-question-choice'><input type='radio' name='answer-19978[]' id='answer-id-77594' class='answer answer-9 php-answer-label answerof-19978' value='77594' \/>&nbsp;<label for='answer-id-77594' id='answer-label-77594' class='php-answer-label answer label-9'><span class='answer'>A random universally unique identifier number (version 4 UUID)<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77595' \/><div class='watu-question-choice'><input type='radio' name='answer-19978[]' id='answer-id-77595' class='answer answer-9 js-answer-label answerof-19978' value='77595' \/>&nbsp;<label for='answer-id-77595' id='answer-label-77595' class='js-answer-label answer label-9'><span class='answer'>The original order identification number from the sales system, which is a monotonically increasing integer<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>https:\/\/cloud.google.com\/spanner\/docs\/schema-and-data-model#choosing_a_primary_key<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(9,this)' id='btn-9' value='See Answer'  \/><input type='hidden' id='questionType9' value='radio' class=''><\/div><div class='watu-question' id='question-10'><div class='question-content'><p><strong>NEW QUESTION 153<\/strong><br \/>When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19979' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77596' \/><div class='watu-question-choice'><input type='radio' name='answer-19979[]' id='answer-id-77596' class='answer answer-10 php-answer-label answerof-19979' value='77596' \/>&nbsp;<label for='answer-id-77596' id='answer-label-77596' class='php-answer-label answer label-10'><span class='answer'>zone<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77597' \/><div class='watu-question-choice'><input type='radio' name='answer-19979[]' id='answer-id-77597' class='answer answer-10 js-answer-label answerof-19979' value='77597' \/>&nbsp;<label for='answer-id-77597' id='answer-label-77597' class='js-answer-label answer label-10'><span class='answer'>node<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77598' \/><div class='watu-question-choice'><input type='radio' name='answer-19979[]' id='answer-id-77598' class='answer answer-10 js-answer-label answerof-19979' value='77598' \/>&nbsp;<label for='answer-id-77598' id='answer-label-77598' class='js-answer-label answer label-10'><span class='answer'>label<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77599' \/><div class='watu-question-choice'><input type='radio' name='answer-19979[]' id='answer-id-77599' class='answer answer-10 js-answer-label answerof-19979' value='77599' \/>&nbsp;<label for='answer-id-77599' id='answer-label-77599' class='js-answer-label answer label-10'><span class='answer'>type<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:<br\/>The project in which the cluster will be created<br\/>The region to use<br\/>The name of the cluster<br\/>The zone in which the cluster will be created<br\/>You can specify many more details beyond these minimum requirements. For example, you can<br\/>also specify the number of workers, whether preemptible compute should be used, and the network settings.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(10,this)' id='btn-10' value='See Answer'  \/><input type='hidden' id='questionType10' value='radio' class=''><\/div><div class='watu-question' id='question-11'><div class='question-content'><p><strong>NEW QUESTION 154<\/strong><br \/>What are the minimum permissions needed for a service account used with Google Dataproc?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19980' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77600' \/><div class='watu-question-choice'><input type='radio' name='answer-19980[]' id='answer-id-77600' class='answer answer-11 js-answer-label answerof-19980' value='77600' \/>&nbsp;<label for='answer-id-77600' id='answer-label-77600' class='js-answer-label answer label-11'><span class='answer'>Execute to Google Cloud Storage; write to Google Cloud Logging<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77601' \/><div class='watu-question-choice'><input type='radio' name='answer-19980[]' id='answer-id-77601' class='answer answer-11 js-answer-label answerof-19980' value='77601' \/>&nbsp;<label for='answer-id-77601' id='answer-label-77601' class='js-answer-label answer label-11'><span class='answer'>Write to Google Cloud Storage; read to Google Cloud Logging<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77602' \/><div class='watu-question-choice'><input type='radio' name='answer-19980[]' id='answer-id-77602' class='answer answer-11 js-answer-label answerof-19980' value='77602' \/>&nbsp;<label for='answer-id-77602' id='answer-label-77602' class='js-answer-label answer label-11'><span class='answer'>Execute to Google Cloud Storage; execute to Google Cloud Logging<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77603' \/><div class='watu-question-choice'><input type='radio' name='answer-19980[]' id='answer-id-77603' class='answer answer-11 php-answer-label answerof-19980' value='77603' \/>&nbsp;<label for='answer-id-77603' id='answer-label-77603' class='php-answer-label answer label-11'><span class='answer'>Read and write to Google Cloud Storage; write to Google Cloud Logging<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.<br\/>Reference: https:\/\/cloud.google.com\/dataproc\/docs\/concepts\/service-<br\/>accounts#important_notes<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(11,this)' id='btn-11' value='See Answer'  \/><input type='hidden' id='questionType11' value='radio' class=''><\/div><div class='watu-question' id='question-12'><div class='question-content'><p><strong>NEW QUESTION 155<\/strong><br \/>You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19981' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77604' \/><div class='watu-question-choice'><input type='radio' name='answer-19981[]' id='answer-id-77604' class='answer answer-12 php-answer-label answerof-19981' value='77604' \/>&nbsp;<label for='answer-id-77604' id='answer-label-77604' class='php-answer-label answer label-12'><span class='answer'>Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline<br \/>on Dataproc to write the data into BigQuery<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77605' \/><div class='watu-question-choice'><input type='radio' name='answer-19981[]' id='answer-id-77605' class='answer answer-12 js-answer-label answerof-19981' value='77605' \/>&nbsp;<label for='answer-id-77605' id='answer-label-77605' class='js-answer-label answer label-12'><span class='answer'>Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the<br \/>data, and then use federated queries from BigQuery for machine learning.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77606' \/><div class='watu-question-choice'><input type='radio' name='answer-19981[]' id='answer-id-77606' class='answer answer-12 js-answer-label answerof-19981' value='77606' \/>&nbsp;<label for='answer-id-77606' id='answer-label-77606' class='js-answer-label answer label-12'><span class='answer'>Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL<br \/>queries to transform the data, and then write the transformations to a new table<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77607' \/><div class='watu-question-choice'><input type='radio' name='answer-19981[]' id='answer-id-77607' class='answer answer-12 js-answer-label answerof-19981' value='77607' \/>&nbsp;<label for='answer-id-77607' id='answer-label-77607' class='js-answer-label answer label-12'><span class='answer'>Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(12,this)' id='btn-12' value='See Answer'  \/><input type='hidden' id='questionType12' value='radio' class=''><\/div><div class='watu-question' id='question-13'><div class='question-content'><p><strong>NEW QUESTION 156<\/strong><br \/>An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19982' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77608' \/><div class='watu-question-choice'><input type='radio' name='answer-19982[]' id='answer-id-77608' class='answer answer-13 js-answer-label answerof-19982' value='77608' \/>&nbsp;<label for='answer-id-77608' id='answer-label-77608' class='js-answer-label answer label-13'><span class='answer'>Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77609' \/><div class='watu-question-choice'><input type='radio' name='answer-19982[]' id='answer-id-77609' class='answer answer-13 js-answer-label answerof-19982' value='77609' \/>&nbsp;<label for='answer-id-77609' id='answer-label-77609' class='js-answer-label answer label-13'><span class='answer'>Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77610' \/><div class='watu-question-choice'><input type='radio' name='answer-19982[]' id='answer-id-77610' class='answer answer-13 js-answer-label answerof-19982' value='77610' \/>&nbsp;<label for='answer-id-77610' id='answer-label-77610' class='js-answer-label answer label-13'><span class='answer'>Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77611' \/><div class='watu-question-choice'><input type='radio' name='answer-19982[]' id='answer-id-77611' class='answer answer-13 php-answer-label answerof-19982' value='77611' \/>&nbsp;<label for='answer-id-77611' id='answer-label-77611' class='php-answer-label answer label-13'><span class='answer'>Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(13,this)' id='btn-13' value='See Answer'  \/><input type='hidden' id='questionType13' value='radio' class=''><\/div><div class='watu-question' id='question-14'><div class='question-content'><p><strong>NEW QUESTION 157<\/strong><br \/>You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19983' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77612' \/><div class='watu-question-choice'><input type='radio' name='answer-19983[]' id='answer-id-77612' class='answer answer-14 php-answer-label answerof-19983' value='77612' \/>&nbsp;<label for='answer-id-77612' id='answer-label-77612' class='php-answer-label answer label-14'><span class='answer'>Cloud SQL<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77613' \/><div class='watu-question-choice'><input type='radio' name='answer-19983[]' id='answer-id-77613' class='answer answer-14 js-answer-label answerof-19983' value='77613' \/>&nbsp;<label for='answer-id-77613' id='answer-label-77613' class='js-answer-label answer label-14'><span class='answer'>Cloud Bigtable<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77614' \/><div class='watu-question-choice'><input type='radio' name='answer-19983[]' id='answer-id-77614' class='answer answer-14 js-answer-label answerof-19983' value='77614' \/>&nbsp;<label for='answer-id-77614' id='answer-label-77614' class='js-answer-label answer label-14'><span class='answer'>Cloud Spanner<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77615' \/><div class='watu-question-choice'><input type='radio' name='answer-19983[]' id='answer-id-77615' class='answer answer-14 js-answer-label answerof-19983' value='77615' \/>&nbsp;<label for='answer-id-77615' id='answer-label-77615' class='js-answer-label answer label-14'><span class='answer'>Cloud Datastore<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(14,this)' id='btn-14' value='See Answer'  \/><input type='hidden' id='questionType14' value='radio' class=''><\/div><div class='watu-question' id='question-15'><div class='question-content'><p><strong>NEW QUESTION 158<\/strong><br \/>Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat<br \/>a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19984' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77616' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19984[]' id='answer-id-77616' class='answer answer-15 php-answer-label answerof-19984' value='77616' \/>&nbsp;<label for='answer-id-77616' id='answer-label-77616' class='php-answer-label answer label-15'><span class='answer'>Create a new view over events using standard SQL<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77617' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19984[]' id='answer-id-77617' class='answer answer-15 js-answer-label answerof-19984' value='77617' \/>&nbsp;<label for='answer-id-77617' id='answer-label-77617' class='js-answer-label answer label-15'><span class='answer'>Create a new partitioned table using a standard SQL query<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77618' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19984[]' id='answer-id-77618' class='answer answer-15 js-answer-label answerof-19984' value='77618' \/>&nbsp;<label for='answer-id-77618' id='answer-label-77618' class='js-answer-label answer label-15'><span class='answer'>Create a new view over events_partitioned using standard SQL<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77619' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19984[]' id='answer-id-77619' class='answer answer-15 js-answer-label answerof-19984' value='77619' \/>&nbsp;<label for='answer-id-77619' id='answer-label-77619' class='js-answer-label answer label-15'><span class='answer'>Create a service account for the ODBC connection to use for authentication<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77620' \/><div class='watu-question-choice'><input type='checkbox' name='answer-19984[]' id='answer-id-77620' class='answer answer-15 php-answer-label answerof-19984' value='77620' \/>&nbsp;<label for='answer-id-77620' id='answer-label-77620' class='php-answer-label answer label-15'><span class='answer'>Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared &#8220;events&#8221;<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(15,this)' id='btn-15' value='See Answer'  \/><input type='hidden' id='questionType15' value='checkbox' class=''><\/div><div class='watu-question' id='question-16'><div class='question-content'><p><strong>NEW QUESTION 159<\/strong><br \/>Which of the following is NOT true about Dataflow pipelines?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='19985' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77621' \/><div class='watu-question-choice'><input type='radio' name='answer-19985[]' id='answer-id-77621' class='answer answer-16 php-answer-label answerof-19985' value='77621' \/>&nbsp;<label for='answer-id-77621' id='answer-label-77621' class='php-answer-label answer label-16'><span class='answer'>Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77622' \/><div class='watu-question-choice'><input type='radio' name='answer-19985[]' id='answer-id-77622' class='answer answer-16 js-answer-label answerof-19985' value='77622' \/>&nbsp;<label for='answer-id-77622' id='answer-label-77622' class='js-answer-label answer label-16'><span class='answer'>Dataflow pipelines can consume data from other Google Cloud services<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77623' \/><div class='watu-question-choice'><input type='radio' name='answer-19985[]' id='answer-id-77623' class='answer answer-16 js-answer-label answerof-19985' value='77623' \/>&nbsp;<label for='answer-id-77623' id='answer-label-77623' class='js-answer-label answer label-16'><span class='answer'>Dataflow pipelines can be programmed in Java<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='77624' \/><div class='watu-question-choice'><input type='radio' name='answer-19985[]' id='answer-id-77624' class='answer answer-16 js-answer-label answerof-19985' value='77624' \/>&nbsp;<label for='answer-id-77624' id='answer-label-77624' class='js-answer-label answer label-16'><span class='answer'>Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Explanation<br\/>Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https:\/\/cloud.google.com\/dataflow\/<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(16,this)' id='btn-16' value='See Answer'  \/><input type='hidden' id='questionType16' value='radio' class=''><\/div><div style='display:none' id='question-17'><br \/><div class='question-content'><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/blog.examboosts.com\/wp-content\/plugins\/watu\/loading.gif\" width=\"16\" height=\"16\" alt=\"Loading ...\" title=\"Loading ...\" \/>&nbsp;Loading &#8230;<\/div><\/div><br \/>\n<input type=\"button\" name=\"action\" onclick=\"Watu.submitResult()\" id=\"action-button\" style=\"margin:0 auto 20px auto;\" value=\"View Results\"  class=\"watu-submit-button\" \/>\n<input type=\"hidden\" name=\"no_ajax\" 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> 0) {\n\tjQuery('.showchecked').show()\n} else {\n\tjQuery('.showchecked').hide()\n}\n<\/script>\n<p>The Google Professional-Data-Engineer exam covers a wide range of topics, including data processing, storage, analysis, transformation, and visualization on Google Cloud Platform. Candidates are expected to have a deep understanding of Google Cloud Platform services and tools, as well as the ability to design and implement scalable, reliable, and efficient data processing systems that meet business requirements. Google Certified Professional Data Engineer Exam certification exam is rigorous and challenging, requiring candidates to demonstrate their ability to apply their knowledge and skills to real-world scenarios. Successful candidates will be able to demonstrate their proficiency in designing and building data processing systems on Google Cloud Platform and will be recognized as experts in this field.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Powerful Professional-Data-Engineer PDF Dumps for Professional-Data-Engineer Questions: <a href=\"https:\/\/www.examboosts.com\/Google\/Professional-Data-Engineer-practice-exam-dumps.html\" target=\"_blank\" rel=\"noopener\">https:\/\/www.examboosts.com\/Google\/Professional-Data-Engineer-practice-exam-dumps.html<\/a><\/strong><\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>[Oct-2024] Free Professional-Data-Engineer Exam Dumps to Improve Exam Score 2024 Realistic Professional-Data-Engineer Dumps Exam Tips Test Pdf Exam Material The Google Professional-Data-Engineer exam covers a wide range of topics, including data processing, storage, analysis, transformation, and visualization on Google Cloud Platform. Candidates are expected to have a deep understanding of Google Cloud Platform services and&hellip; <br \/> <a class=\"button small blue\" href=\"https:\/\/blog.examboosts.com\/zh\/2024\/10\/oct-2024-free-professional-data-engineer-exam-dumps-to-improve-exam-score-q144-q159\/\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":2638,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34,2534],"tags":[6940,6939,6938,6941],"class_list":["post-2637","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-google","category-professional-data-engineer","tag-new-professional-data-engineer-test-sample-questions","tag-professional-data-engineer-latest-practice-questions-files","tag-professional-data-engineer-latest-test-collection-sheet","tag-professional-data-engineer-valid-exam-cram-review"],"_links":{"self":[{"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/posts\/2637","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/comments?post=2637"}],"version-history":[{"count":1,"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/posts\/2637\/revisions"}],"predecessor-version":[{"id":2725,"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/posts\/2637\/revisions\/2725"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/media\/2638"}],"wp:attachment":[{"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/media?parent=2637"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/categories?post=2637"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.examboosts.com\/zh\/wp-json\/wp\/v2\/tags?post=2637"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}